Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training

• URL: http://arxiv.org/abs/2511.07328v1
• Date: Mon, 10 Nov 2025 17:31:02 GMT
• Title: Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
• Authors: Artyom Sorokin, Nazar Buzun, Alexander Anokhin, Oleg Inozemcev, Egor Vedernikov, Petr Anokhin, Mikhail Burtsev, Trushkov Alexey, Yin Wenshuai, Evgeny Burnaev,
• Abstract summary: We propose Q-RAG, a novel approach that fine-tunes Embedder model for multi-step retrieval using reinforcement learning (RL)<n>Q-RAG offers a competitive, resource-efficient alternative to existing multi-step retrieval methods for open-domain question answering.
• Score: 50.37345200692884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval approaches have emerged, typically involving the fine-tuning of small LLMs to perform multi-step retrieval. This type of fine-tuning is highly resource-intensive and does not enable the use of larger LLMs. In this work, we propose Q-RAG, a novel approach that fine-tunes the Embedder model for multi-step retrieval using reinforcement learning (RL). Q-RAG offers a competitive, resource-efficient alternative to existing multi-step retrieval methods for open-domain question answering and achieves state-of-the-art results on the popular long-context benchmarks Babilong and RULER for contexts up to 10M tokens.

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